Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning for predicting doctors' diagnoses from CTNs, a task known as ICD coding. Data annotation is time consuming, particularly when a degree of specialization is needed, as is the case for medical data. This paper presents a method of augmenting a sparsely annotated dataset of Icelandic CTNs with a machine-learned imputation in a semi-self-supervised manner. We train a neural network on a small set of annotated CTNs and use it to extract clinical features from a set of un-annotated CTNs. These clinical features consist of answers to about a thousand potential questions that a physician might find the answers to during a consultation of a patient. The features are then used to train a classifier for the diagnosis of certain types of diseases. We report the results of an evaluation of this data augmentation method over three tiers of data availability to the physician. Our data augmentation method shows a significant positive effect which is diminished when clinical features from the examination of the patient and diagnostics are made available. We recommend our method for augmenting scarce datasets for systems that take decisions based on clinical features that do not include examinations or tests.

Clinical coding is the task of assigning a set of alphanumeric codes, referred to as ICD (International Classification of Diseases), to a medical event based on the context captured in a clinical narrative. The latest version of ICD, ICD-10, includes more than 70,000 codes. As this is a labor-intensive and error-prone task, automatic ICD coding of medical reports using machine learning has gained significant interest in the last decade. Existing literature has modeled this problem as a multi-label task. Nevertheless, such multi-label approach is challenging due to the extremely large label set size. Furthermore, the interpretability of the predictions is essential for the endusers (e.g., healthcare providers and insurance companies). In this paper, we propose a novel approach for automatic ICD coding by reformulating the extreme multi-label problem into a simpler multi-class problem using a hierarchical solution. We made this approach viable through extensive data collection to acquire phrase-level human coder annotations to supervise our models on learning the specific relations between the input text and predicted ICD codes. Our approach employs two independently trained networks, the sentence tagger and the ICD classifier, stacked hierarchically to predict a codeset for a medical report. The sentence tagger identifies focus sentences containing a medical event or concept relevant to an ICD coding. Using a supervised attention mechanism, the ICD classifier then assigns each focus sentence with an ICD code. The proposed approach outperforms strong baselines by large margins of 23% in subset accuracy, 18% in micro-F1, and 15% in instance based F-1. With our proposed approach, interpretability is achieved not through implicitly learned attention scores but by attributing each prediction to a particular sentence and words selected by human coders.